Brain disease diagnosis system

ABSTRACT

To perform a more accurate and detailed diagnosis of a brain disease, a diagnosis server of the brain disease diagnosis system for diagnosing a brain disease of an examined person includes: an acquiring unit for acquiring a brain image of the examined person so as to obtain an acquired image; a region setting unit for setting a plurality of regions in the acquired image; an individual index value calculating unit for calculating an individual index value based on a pixel value of the acquired image, in each of the plurality of regions; a whole index value calculating unit for calculating a whole index value by weighting the individual index value of each of the plurality of regions; a diagnosis unit for diagnosing the brain disease of the examined person based on the whole index value; and an output unit for outputting information indicating a diagnosis outcome.

TECHNICAL FIELD

The present invention relates to a brain disease diagnosis systemdiagnosing a brain disease of an examined person.

BACKGROUND ART

Along with the development of medical technology, various informationsuch as patient information and image information are being convertedinto electronic data. In particular, along with the development ofmedical devices, the image data amount has increased, and in responsethereto, the burden imposed on doctors to interpret radiograms hasincreased. Under such circumstances, an automatic diagnosis system(Computer-aided diagnosis (CAD)) intended to assist a doctor in theinterpretation of a radiogram is being researched and developed targetedfor various modalities such as Computed Tomography (CT), MagneticResonance Imaging (MRI), ultrasonography (US), Single Photon EmissionComputed Tomography (SPECT), and Positron Emission Tomography (PET), orsites (a lung field, a breast, etc.).

As a method for diagnosing a brain disease in the automatic diagnosissystem, there is proposed a method described in Patent Literature 1. Inthis method, a cinerea tissue is extracted from a subject's brain imagedata obtained from MRI, PET, SPECT, etc., and the extracted brain imageis smoothened. Thereafter, anatomic standardization, etc., are performedon the brain image to statistically compare the brain image of thesubject and that of a healthy person after which a Region of Interest(ROI) is set for analysis. Then, the analysis result is provided as adiagnostic outcome. In this case, a region set as the ROI isautomatically set based on a Z score calculated from an average valueand a standard deviation of voxel values of a healthy person imagecluster about each voxel of the brain image.

CITATION LIST Patent Literature

-   Patent Literature 1: Japanese Published Unexamined Patent    Application No. 2005-237441

SUMMARY OF INVENTION Technical Problem

In the method described in the Patent Literature 1, a brain disease isdiagnosed based on the analysis in which the automatically set singleROI is uniformly treated. However, it is probable that the analysis ofonly the single ROI does not permit exact diagnosis. Moreover, it is notpossible to perform a detailed diagnosis to determine what brain diseaseoccurs. This is due to the fact that when only the single ROI isanalyzed, it is not possible to determine whether the data of the ROIresults from an influence of a brain disease, nor is it possible todetermine as to whether it results from an influence of what braindisease (whether it results from an influence of Alzheimer's, forexample).

The present invention has been achieved to solve the above-describedproblems, and an object thereof is to provide a brain disease diagnosissystem capable of performing a more accurate and detailed diagnosis of abrain disease.

Solution to Problem

In order to achieve the above-described object, a brain diseasediagnosis system according to the present invention is a brain diseasediagnosis system for diagnosing a brain disease of an examined personwhich includes: acquiring means for acquiring a brain image of theexamined person so as to obtain an acquired image; region setting meansfor setting a plurality of regions in the acquired image acquired by theacquiring means; individual index value calculating means forcalculating an individual index value based on a pixel value of theacquired image, in each of the plurality of regions set by the regionsetting means; whole index value calculating means for calculating awhole index value by weighting the individual index value of each of theplurality of regions calculated by the individual index valuecalculating means; diagnosis means for diagnosing the brain disease ofthe examined person based on the whole index value calculated by thewhole index value calculating means; and output means for outputtinginformation indicating a diagnosis outcome issued by the diagnosismeans.

In the brain disease diagnosis system according to the presentinvention, based on the image of the brain of the examined person, thebrain disease of the examined person is diagnosed. In this system, aplurality of regions are set to the brain image, and individual indexvalues based on the pixel value are calculated for each of the pluralityof regions. Then, each of the individual index values is weighted sothat a whole index value is calculated after which the above-describeddiagnosis is performed from the whole index value. Therefore, based onthe brain disease diagnosis system according to the present invention,it is possible to make a determination in which an influence of a braindisease is considered for each brain region and also possible to performa more accurate and detailed diagnosis of the brain disease.

Desirably, the diagnosis means diagnoses the brain disease of theexamined person by comparing a threshold value obtained based on anindex value of sample data having the brain disease and an index valueof sample data not having the brain disease and the whole index valuecalculated by the whole index value calculating means. According to thisconfiguration, the criteria for determination at the time of thedetermination using the whole index value can be made more appropriate,and further, it is possible to perform a more accurate and detaileddiagnosis of the brain disease.

Desirably, the whole index value calculating means performs theweighting based on the index value of the sample data having the braindisease and the index value of the sample data not having the braindisease. According to this configuration, the weighting can be performedmore appropriately at the time of the calculation of the whole indexvalue. For example, a large weighting can be applied to a range wherethe brain disease to be diagnosed is greatly influenced. As a result, itis possible to perform a more accurate and detailed diagnosis of thebrain disease.

Desirably, the acquiring means corrects the image based on the pixelvalue of the acquired brain image so as to obtain the acquired image.According to the configuration, the brain disease can be appropriatelydiagnosed by removing an individual variability, etc., of the brain ofthe examined person.

Desirably, the acquiring means also acquires information indicating theage of the examined person, and the diagnosis means diagnoses the braindisease of the examined person according to the age of the examinedperson indicated by the information acquired by the acquiring means.Although, generally, a state of a brain changes according to age,according to this configuration, it is possible to appropriatelydiagnose a brain disease according to age.

Desirably, the acquiring means anatomically standardizes the acquiredbrain image so as to obtain the acquired image. According to thisconfiguration, it is possible to appropriately diagnose a brain diseaseby facilitating the processing of the brain image.

Desirably, the brain disease diagnosis system further includes imagingmeans for imaging the brain image of the examined person, wherein theacquiring means acquires the image imaged by the imaging means.According to this configuration, the brain image can be reliablyacquired, and thus, the present invention can be reliably implemented.

Desirably, the imaging means images a slice image of a brain of theexamined person as the brain image of the examined person, and theacquiring means produces from the slice image imaged by the imagingmeans a brain surface projection image obtained by projecting a brainsurface so as to obtain the acquired image. According to thisconfiguration, based on the brain surface image that facilitates thediagnosis for some type of a brain disease, the diagnosis can beperformed.

Advantageous Effects of Invention

According to the present invention, it is possible to make adetermination while considering an influence of a brain disease for eachbrain region, thus performing a more accurate and detailed diagnosis ofthe brain disease.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram showing the configuration of a brain diseasediagnosis system according to an embodiment of the present invention.

FIG. 2 is a view showing a brain image utilized in the brain diseasediagnosis system.

FIG. 3 is a graph showing a transition of whole cerebral metabolicamounts by aging, based on a standard brain image.

FIG. 4 is a histogram of SUV of an Alzheimer's diseased brain and anormal brain.

FIG. 5 is a graph showing an average value of SUV of an Alzheimer'sdiseased brain and a normal brain, for each of a plurality of examinedpersons.

FIG. 6 are views showing an image according to a Z score obtained from abrain surface projection image of a patient with Alzheimer's diseasewhen a brain surface projection image of a normal brain is to becompared, and also showing a brain site with a high Z score.

FIG. 7 is a diagram showing a functional configuration of a diagnosisserver of a brain disease diagnosis system.

FIG. 8 are views showing an anatomical site of a brain.

FIG. 9 is a graph showing an average value of SUV, calculated for eachbrain region, of an Alzheimer's diseased brain and a normal brain, foreach of a plurality of examined persons.

FIG. 10 is a histogram of SUV of an Alzheimer's diseased brain and anormal brain, for each brain region.

FIG. 11 is a histogram of SUV of an Alzheimer's diseased brain and anormal brain, for each brain region.

FIG. 12 are a graph showing an average value of SUV of a whole brain ofan Alzheimer's diseased brain and a normal brain, for each of aplurality of examined persons, and a graph showing a relationshipbetween a threshold value, and a sensitivity and a specificity.

FIG. 13 is a graph showing a relationship between a threshold value, anda sensitivity and a specificity, for each brain region.

FIG. 14 is a graph showing a whole index value of an Alzheimer'sdiseased brain and a normal brain, for each of a plurality of examinedpersons, and a graph showing a relationship between a threshold value,and a sensitivity and a specificity.

FIG. 15 is a flowchart showing processing executed in the brain diseasediagnosis system according to an embodiment of the present invention.

DESCRIPTION OF EMBODIMENTS

Hereinafter, together with diagrams, a suitable embodiment of a braindisease diagnosis system according to the present invention will beexplained in detail. It is noted that in the description of drawings,like components are denoted by like numerals and overlappingdescriptions will be omitted.

FIG. 1 shows the configuration of a brain disease diagnosis system 1according to the embodiment. The brain disease diagnosis system 1 is asystem diagnosing a brain disease of an examined person. That is, thebrain disease diagnosis system 1 is a system for determining whether theexamined person suffers from a brain disease. Examples of a braindisease subject to diagnosis may include a disease the type of which isspecified such as Alzheimer's disease and a disease the type of which isnot specified in which whether an abnormality occurs in a brain is notknown. In this embodiment, Alzheimer's disease is used as an example.

As shown in FIG. 1, the brain disease diagnosis system 1 is configuredto include a diagnosis server 10 that serves a primary function of thebrain disease diagnosis system 1. Moreover, it is desired that the braindisease diagnosis system 1 includes a storage system in which medicalimage data imaged by modalities such as a CT machine, an MRI machine,and a PET machine is accommodated and managed, or the system 1 isconnected to these machines. In this embodiment, as described below, thebrain disease diagnosis system 1 includes the storage system; however,the system 1 does not necessarily include the storage system.

The brain disease diagnosis system 1 (as the configuration of thestorage system) includes: a plurality of storages 20 for accommodatingimage data; a plurality of load balancers 30; a plurality of imageservers 40; a plurality of business servers 50; and a plurality ofgateway servers 60.

Each load balancer 30 is a device for performing various processing suchas receiving a task request input to the storage system, and in responseto the task request, transferring it to each of the servers 40, 50, and60 of the storage system. Each load balancer 30 performs loaddistribution control so that each of the servers 40, 50, and 60 is notoverloaded for each task request. The load balancer 30, the storages 20,and each of the servers 40, 50, and 60 are connected through a wiredline via a switching hub 70 so that information can be transmitted andreceived to and from each other. It is noted that one of the two loadbalancers 30 is used as a back-up, for example, in a case where one ofthe two experiences a failure.

Each image server 40 is a device for recording and managing informationabout the image data. Specifically, the information about the image dataindicates in which of the storages 20, out of the plurality of storages20, the image data is accommodated (accommodation storage information).The image server 40 is input with a write request and a read request ofthe information from the load balancer 30, and performs processing inresponse to the requests. Moreover, the image servers 40 are connectedto each other through a wired line, and transmit and receive theinformation to and from each other. Between the image servers 40, theinformation is synchronized. Further, the image server 40 performsprocessing on an image imaged by the modalities, as described in moredetail below.

Each business server 50 is a device for recording and managinginformation of the examined person about the image data. The businessserver 50 is input with a task request for processing about theinformation of the examined person from the load balancer 30, andperforms processing in response to the request. Further, similar to theimage server 40, the business servers 50 are connected through a wiredline, and can transmit and receive the information to and from eachother.

Each gateway server (DICOM gateway) 60 is a device that is input withthe image data accommodated in the storages 20 from the load balancer 30and transfers it to the storage 20. Each of the gateway servers 60outputs the image data to the load balancer 80 connected to the storage20. It is noted that this load balancer 80 is different from the loadbalancer 30 connected to each of the servers 40, 50, and 60. Further,when information used for diagnosing the brain disease according to theembodiment is input, the gateway server 60 inputs the information to thediagnosis server 10.

The load balancer 80 determines which of the storages 20 in which theimage data input from the gateway server 60 or the diagnosis server 10or data indicating the diagnostic outcome should be accommodated, andaccommodates the data into the determined storage 20. Generally, theabove-described data is accommodated in the two storages 20 in order toprevent a data loss. It is noted that one of the two load balancers 80is used as a back-up, for example, in a case where one of the twoexperiences a failure.

Moreover, the brain disease diagnosis system 1 may include, as a deviceused by a user, a client 100, a viewer 110, and modalities 121, 122, and123 in a manner to be connected to the storage system. Each of theabove-described devices 100, 110, and 121 to 123 is connected to theload balancer 30 through a wired line via the switching hub 130 so thatinformation can be transmitted and received to and from the loadbalancer 30.

The client 100 is a terminal used when the user utilizes the braindisease diagnosis system 1. The viewer 110 is a terminal forinterpretation of a radiogram, for the user such as a doctor tointerpret the image data. In the client 100 or the viewer 110, means foracquiring the image data and displaying the acquired image data isincluded. Moreover, the client 100 and the viewer 110 are used also forthe user to refer to the diagnostic outcome provided by the braindisease diagnosis system 1. The client 100 and the viewer 110 input (byuser's manipulation, for example) a request for acquiring the image datainto the load balancer 30 in order to acquire the image data. In therequest for acquiring, information for specifying the image data to beacquired is included.

The modalities 121 to 123 are machines or imaging means for imaging ahead (brain) of the examined person to acquire the image data, andspecifically are a CT machine 121, an MRI machine 122, and a PET machine123, for example. In order to manage the acquired image data in thestorage system, the modalities 121 to 123 send a request for registeringthe image data to the load balancer 30.

Further, the image data imaged by the modality 121 is a slice image, cutalong a predetermined cross section, showing the interior of the head(brain) of the examined person. It is noted that the slice imagedesirably includes a plurality of slice images on a cross section atintervals of several mm to several cm, for example. Pixels configuringthe slice image have intensity (pixel value) according to tissues or aregion in the head, at a position corresponding to the pixels. Forexample, a pixel value of the slice image acquired by the PET machine123 is a value according to Standard Update Value (SUV) orsemi-quantitative amount indicating a cerebral glucose metabolic amount(SUV can be obtained by performing a predetermined arithmeticcalculation on the pixel value. Alternatively, the pixel value itselfcan be used as a value indicating SUV). That is, the slice image used inthe embodiment includes a pixel value according to a function dependingon a position of the brain. In the embodiment, the slice image imaged bythe above-described PET machine 123 is used for diagnosing the braindisease. Further, when the slice image is acquired by the PET machine123, information indicating an age of the examined person is input as aresult of input by a manipulator of the PET machine 123, for example,and the input information is made to correspond to the above-describedslice image.

Specific examples of the above-described diagnosis server 10, storages20, load balancers 30 and 80, image server 40, business server 50,gateway server 60, client 100, and viewer 110 include a computerprovided with Central Processing Unit (CPU), a memory, a disc device,etc. When these are operated, functions of the respective devices aredemonstrated.

Herein, a brain image processed in the brain disease diagnosis system 1and information shown about the brain image will be explained in detail.A content to be explained here includes knowledge obtained throughstudies by the inventors of the subject application.

An image initially acquired in the brain disease diagnosis system 1 isbased on the examined person's slice image acquired by the PET machine123. In the brain disease diagnosis system 1, processing for imaging bythe PET machine 123, and processing in which the image taken by the PETmachine 123 is adopted as an acquired image (described later) by thediagnosis server 10 are performed. These processings are describedbelow. It is noted that when the slice image is input to the diagnosisserver 10, the information indicating the age of the examined person isalso input in a corresponding manner, as described above, which makesthe information indicating the age of the examined person available.

The PET machine 123 images (the brain of) the examined person to acquirea slice image (PET raw image) 201 shown in FIG. 2. The PET machine 123outputs the acquired slice image 201 to the diagnosis server 10 via theswitching hub 130, the load balancer 30, the switching hub 70, and thegateway server 60. The diagnosis server 10 anatomically standardizes theinput slice image 201 to obtain a standard brain image (tomographicimage) 202 shown in FIG. 2. It is noted that in a case where the sliceimage acquired by the PET machine 123 is an image showing an entirehuman body, the diagnosis server 10 cuts out only a head image necessaryfor the diagnosis.

In this case, the anatomic standardization may be performed by aconventional method, and specifically, it is performed by using a toolof three dimensional stereotactic surface projection (3DSSP), forexample. Subsequently, the diagnosis server 10 performs mask processingon the standard brain image 202 and cuts surrounding noise to extractimage data inside the brain. It is noted that the above-described maskprocessing can be performed by using a conventional method. However, itis not always necessary to implement the above-described maskprocessing.

Subsequently, the diagnosis server 10 performs correction processing onthe standard brain image 202 on which the anatomic standardization orthe mask processing has been performed. In this correction processing,standardization is performed to alleviate a variation of the pixelvalues among the images. Specifically, the standard brain image 202 iscorrected (for example, the pixel value is multiplied by a coefficientthat could make an average of the pixel values a predetermined value) sothat an average of the pixel values of the whole brain is a previouslyset predetermined value or a constant value. It is noted that theaverage of the pixel values of the whole brain may not always beconstant; for example, the standard brain image 202 may be corrected sothat an average of pixel values of a portion equivalent to a cerebellum,a pons or a thalami of the standard brain image 202, for example, is thepreviously set predetermined value.

Subsequently, the diagnosis server 10 generates a brain surface image203 indicating a brain surface, as shown in FIG. 2, from the correctedstandard brain image 202. The diagnosis server 10 generates a brainsurface projection image 204 obtained by projecting the generated brainsurface image from each direction. The brain surface projection image204 is generated for each projected direction, and the directions areeight directions, i.e., right; left; upper; lower; front; rear; andright and left cut at the center, as shown in FIG. 2. Specifically, thebrain surface image 203 and the brain surface projection image 204 aregenerated by processing utilizing a tool by the above-described 3DSSP.

Subsequently, processing further performed on the standard brain image202 in the diagnosis server 10 will be explained. In the diagnosisserver 10, the brain surface projection image 204 is generated from thestandard brain image 202 according to a method similar to that describedabove. Next, an image 205 obtained by comparing the brain of theexamined person to a normal brain is generated. A normal brain is abrain of an examined person who does not suffer from a brain disease.The brain surface projection image of a normal brain, similar to thatdescribed above, is previously acquired by the present system 1, etc.,and accommodated in the storages 20 available. The diagnosis server 10acquires the brain surface projection image of a normal brain from thestorages 20. The brain surface projection images of a normal brainaccommodated in the storages 20 are accommodated chronologically, andbased on the information indicating the age of the examined person, thebrain surface projection image of a normal brain at the same generationof the examined person is acquired. The diagnosis server 10 calculatesan average value and a standard deviation of pixel values of theacquired brain surface projection image of a normal brain. Subsequently,based on the calculated value, the diagnosis server 10 calculates a Zscore by using the equation below for each pixel of the brain surfaceprojection image of the brain of the examined person. It is noted thatan equation for calculating the Z score is previously stored in thediagnosis server 10.Z score=(pixel value of brain of examined person-normal brain averagevalue)/normal brain standard deviation

The Z score indicates a degree by which the pixel value differs from thepixel value of a normal brain. When the PET image is used, the higherthe value of the Z score, the lower the glucose metabolic amount. Thediagnosis server 10 generates an image 205 obtained by comparing thebrain of the examined person shown in FIG. 2 to a normal brain, in whichthe calculated Z score is used as the pixel value.

The diagnosis server 10 accommodates the brain image (image data)generated in this way, into the storages 20. It is noted that uponaccommodation into the storages 20, the information indicating the ageof the examined person and indicating whether the examined person hashad a brain disease, etc., is accommodated in the storages 20 in amanner to correspond to the brain image so that the generated brainimage can be used as comparison data when a brain of another examinedperson is diagnosed.

The examined person's slice image acquired by the PET machine 123 isalso managed by the image server 40 of the storage system andaccommodated in the storages 20.

Herein, each brain image and the information shown about the brain imagewill be explained in detail. FIG. 3 shows a graph indicating atransition of the whole cerebral metabolic amounts by aging, based onthe standard brain image 202. In this graph, a horizontal axis indicatesSUV and a vertical axis indicates the frequency (pixel number) of thecorresponding SUV in the standard brain image 202. The higher thefrequency with large SUV, the larger the cerebral glucose metabolicamount. This graph is obtained by counting the frequency when the wholebrain is the target, and provides an average of data of a plurality ofexamined persons (data items of 291 persons (237 men and 54 women) intheir 30's, 397 persons (303 men and 94 women) in their 40's, 249persons (121 men and 128 women) in their 50's, and 30 persons (30 men)in their 60's, respectively). As shown in FIG. 3, SUV is distributed sothat SUV becomes smaller as the generations become older, and thisindicates that the cerebral glucose metabolic amount becomes smaller asthe generations become older. As described above, SUV in the brain imagechanges according to the age of the examined person.

FIG. 4 shows a graph indicating a difference in whole cerebral metabolicamounts between a normal brain and an Alzheimer's diseased brain, basedon the standard brain image 202. In this graph, a horizontal axisindicates SUV and a vertical axis indicates the frequency (pixel number)of the corresponding SUV in the standard brain image 202. In this graph,the frequency is counted while using the whole brain as the target.Further, data indicated by a thin line in this graph is data ofindividual examined persons while data indicated by a thick line is dataobtained by averaging the data of the examined persons, for each of anAlzheimer's diseased brain and a normal brain (data of 23 examinedpersons with a normal brain (12 men and 11 women, the average age 52.3years old) and 24 examined persons with Alzheimer's disease (10 men and14 women, the average age 58.3 years old), respectively). FIG. 5 is agraph showing an average value (vertical axis) of SUV, for each of theexamined persons with an Alzheimer's diseased brain and with a normalbrain. In the graph shown in FIG. 5, there is a significant difference(in average value of SUV) between a brain image of Alzheimer's diseaseand that of a normal brain where the level of significance is less than0.001. As described above, depending on whether the examined personsuffers from Alzheimer's disease, SUV in the brain image differs.

FIG. 6( a) shows an image that is according to the Z score calculated asdescribed above where an average of the pixel values of the brainsurface projection images of a plurality of patients with Alzheimer'sdisease (24 patients) is a target to be compared with an average valueof the pixel values of the brain surface projection images of aplurality of patients with a normal brain (23 patients) (It is notedthat the image is similar to the image 205 in FIG. 2). As shown in theimage, portions with a high Z score, i.e., portions with a largedifference in pixel value between an Alzheimer's diseased brain and anormal brain, are biased. The portions with a high Z score includeportions of a cerebral parietal lobe, temporal lobe, parietalassociation area, and posterior cingulate gyrus (FIG. 6( b) showscorresponding locations of the brain image). As shown in the abovefigure, between an Alzheimer's diseased brain and a normal brain, thedifference in the cerebral glucose metabolic amount corresponds to acerebral site. Thus, the brain image processed in the brain diseasediagnosis system 1 and the information shown about the brain image havebeen explained.

Subsequently, the functions, particularly functions of the diagnosisserver 10, according to the present invention will be explained indetail. As shown in FIG. 7, the diagnosis server 10 is configured toinclude: an acquiring unit 11; a region setting unit 12; an individualindex value calculating unit 13; a whole index value calculating unit14; a diagnosis unit 15; and an output unit 16.

The acquiring unit 11 is acquiring means for acquiring the brain imageof the examined person so as to obtain an acquired image. The imageacquired by the acquiring unit 11 is an image which is acquired by thePET machine 123 and which is based on the examined person's slice image.As described above, the acquiring unit 11 is input with the slice image201 via the gateway server 60 from the PET machine 123, and generatesfrom the slice image 201 the standard brain image 202, the brain surfaceimage 203, and the brain surface projection image 204 in this order bythe above-described processing. The acquiring unit 11 uses theabove-described standard brain image 202 and the brain surfaceprojection image 204 as the acquired image used for diagnosing a braindisease. The acquiring unit 11 outputs the generated acquired image tothe region setting unit 12. It is noted that the acquiring unit 11 doesnot always need to use the image on which the imaging processing hasbeen performed as the acquired image, as described above. If the inputbrain image does not require imaging processing such as standardizationand correction, then the respective imaging processing does not need tobe performed (for example, the input brain image may be used as theacquired image).

The region setting unit 12 is region setting means for setting aplurality of regions (Regions of Interest: ROI) in the acquired image(standard brain image) input from the acquiring unit 11. When the regionis set in the acquired image, the region is divided into cerebralanatomical sites, for example. FIGS. 8( a) and 8(b) show the cerebralanatomical sites. As shown in FIG. 8( a) depicting a right-side surface,the cerebral site includes a frontal lobe 301, a parietal lobe 302, atemporal lobe 303, an occipital lobe 304, and a cerebellum 305. Further,as shown in FIG. 8( b) depicting the right side obtained by cuttingaround the center of the brain, the cerebral site also includes afrontal association area 306, a posterior cingulate gyms 307, and aparietal association area 308. The whole brain may be set as a singleregion (in that case, an overlapping portion between the regions mayoccur). In the region setting, for example, setting such thatcoordinates in the image belong to which of the anatomical sites ispreviously performed (the information indicating the above is previouslyheld), and then, based on the setting, the region is set. This settingis performed by an administrator, a doctor, etc., of the brain diseasediagnosis system 1.

As a standard for specifying the anatomical site, an atlas of Talairachalso used for standard brain conversion of 3DSSP and a brain map ofBrodmann ([Korbinian Brodmann] Brodmann's Localisation in the CerebralCortex: The Principles of Comparative Localisation in the cerebralCortex Based on Cytoarchitectonics) showing anatomical andcytoarchitectural categories of the cerebral cortex (classified from 1to 52) may be used. Further, based on the brain map of Brodmann, severalregions may be combined so that the regions are segmented in a largeregion such as a frontal lobe, a temporal lobe, a parietal lobe, anoccipital lobe, and a cerebellum.

There is no need that the setting of a region is performed on the wholebrain image, and the setting may be performed on a portion where thepixel value specifically changes due to a disease subject to diagnosis.For example, in the case of Alzheimer's disease, the parietal lobe, thetemporal lobe, the parietal association area, and the posteriorcingulate gyrus tend to change more easily, and thus, the respectiveregions may be set on these portions. That is, it is preferable that theregion be set on a site where a change specific to the brain diseasethat is subject to examination is recognized. Conversely, the region mayalso be set on a site where the change specific to the brain diseasethat is subject to examination is not recognized. Moreover, the regionmay also be set on a site where the doctor often observes, andconversely, the region may be set on a site where the doctor seldomobserves.

When the region is set, it may also be possible that the brain image ofa normal brain acquired as a sample and the image of a brain thatsuffers from a disease subject to diagnosis are previously Z-scored foreach pixel, as described above, and the region is automatically set foreach value of the Z score, i.e., 0 to 0.5, 0.5 to 1.0, etc.

The region setting unit 12 outputs the acquired image and theinformation indicating the plurality of set regions, to the individualindex value calculating unit 13.

The individual index value calculating unit 13 is individual index valuecalculating means for calculating, based on the pixel value of theacquired image, an individual index value of each of the plurality ofregions set by the region setting unit 12. Specifically, the individualindex value calculating unit 13 calculates the individual index value asfollows: First, for each region set in the acquired image, frequenciesof the pixel for each pixel value, as a value indicating SUV, areplotted in a histogram, as shown in FIG. 4. The individual index valuecalculating unit 13 calculates an average value of SUV, a standarddeviation, a maximum value, a minimum value, an integral value, and adistribution pattern, from the histogram, and uses them as theindividual index value.

In the embodiment, an example in which the average value of SUV and thestandard deviation are used as the individual index value will beexplained. Also, the individual index value may also be evaluated asfollows: from sample data of an average value of a normal brain, anaverage value N_(ave) and standard deviation NL_(SD) of the pixel valueare previously calculated, and by using these values and the followingequation, an individual pixel conversion value x′ of an average value xof the pixel value of the acquired image of the examined person isevaluated and used as the individual index value.

$\begin{matrix}{x^{\prime} = {{\frac{1}{{NL}_{SD}} \times x} - \frac{{NL}_{ave}}{{NL}_{SD}}}} & \left\lbrack {{Equation}\mspace{14mu} 1} \right\rbrack\end{matrix}$

The individual index value calculating unit 13 outputs informationindicating the calculated individual index value to the whole indexvalue calculating unit 14.

The whole index value calculating unit 14 is whole index valuecalculating means for weighting the individual index value of eachregion calculated by the individual index value calculating unit 13 soas to calculate a whole index value. Specifically, as shown in thefollowing equation, the whole index value is calculated as follows: aweight coefficient kn set to each individual index value Rn (n is anumeral indicating a region) and the corresponding individual indexvalue are multiplied and the resultant values are totaled.Whole index value=R1×k1+R2×k2+R3×k3+ . . . +Rn×kn

Moreover, the above-described weight coefficient kn is desirably setbased on an index value of sample data (of a brain image) having thebrain disease and an, index value of sample data (of a brain image) nothaving the brain disease. The individual index value and the whole indexvalue, and an attitude toward the diagnosis in the present inventionwill be explained in detail later. The weight coefficient is desirablyset in a manner to significantly influence the whole index value in aregion where there is a large difference (there is a tendency to differ)in individual index value between the brain of a cerebral disease and anormal brain. The whole index value calculating unit 14 outputsinformation indicating the calculated whole index value to the diagnosisunit 15.

The diagnosis unit 15 is diagnosis means for diagnosing a brain diseaseof the examined person based on the whole index value calculated by thewhole index value calculating unit 14. Specifically, the diagnosis unit15 compares a threshold value and the whole index value so as todiagnose whether the examined person suffers from a brain disease thatis subject to examination. The above-described threshold value ispreviously stored by the diagnosis unit 15. In this case, theabove-described threshold value is desirably a value obtained based onthe index value of the sample data having a brain disease and the indexvalue of the sample data not having the brain disease. Moreover, thediagnosis unit 15 may make a diagnosis to determine from the whole indexvalue the degree by which the examined person suffers from the braindisease that is subject to examination. The diagnosis unit 15 outputsinformation indicating the diagnosis outcome to the output unit 16.

The output unit 16 is output means for outputting information indicatingthe diagnosis outcome by the diagnosis unit 15. Specifically, the outputunit 16 provides that the doctor, etc., can refer to the diagnosticoutcome by displaying the diagnostic outcome on a display device (notshown) arranged in the diagnosis server 10. Moreover, during thereference, the output unit 16 may display an image used for thediagnosis or data (age, etc.) of the examined person as well. Further,the information output by the output unit 16 may be accommodated in thestorages 20 after which it is output. In this case, the data generatedand calculated by each function is preferably accommodated in thestorage at each calculation and generation.

Subsequently, processing from calculation of the whole index value bythe whole index value calculating unit 14 to diagnosis of a braindisease by the diagnosis unit 15 will be explained in detail.

FIG. 9 is a graph showing an average value (vertical axis) of SUV of anAlzheimer's diseased brain and a normal brain, for each of a pluralityof examined persons, calculated for each brain region shown in FIG. 8(the same graph as that in FIG. 5). In the graph shown in FIG. 9, thereis a significant difference (in average value of SUV) between an imageof an Alzheimer's diseased brain and that of a normal brain with thefollowing levels of significance, respectively. The levels of thesignificance are as follows: less than 0.001 in the parietal lobe (2),less than 0.001 in the temporal lobe (3), less than 0.005 in theoccipital lobe (4), less than 0.001 in the cerebellum (5), less than0.005 in the frontal association area (6), less than 0.001 in theposterior cingulate gyms (7), and less than 0.001 in the parietalassociation area (8), respectively. It is noted that there is no levelof significant in the frontal lobe (1).

Further, FIG. 10 and FIG. 11 are histograms (of an average of aplurality of examined persons) of SUV of an Alzheimer's diseased brain(indicated by AD in the figure) and a normal brain (indicated by Non-ADin the figure), for each region of the brain shown in FIG. 8. As canalso be seen from the above-described FIGS. 9 to 11, the difference inSUV detected between an Alzheimer's diseased brain and a normal braindiffers depending on the site. Specifically, as described in theexplanation in FIG. 6 using the Z score, there is a great difference inparietal lobe, temporal lobe, posterior cingulate gyms, and parietalassociation area between an Alzheimer's diseased brain and a normalbrain.

At this time, diagnosis of the brain disease in which the image of thewhole brain is uniquely processed is considered. FIG. 12( a) is a graphshowing an average value (vertical axis) of SUV of an Alzheimer'sdiseased brain and a normal brain, for each of a plurality of examinedpersons, calculated from the above-described acquired image when thewhole brain is a target. FIG. 12( a) is a graph with data obtained from23 examples of a normal brain and 24 examples of an Alzheimer's diseasedbrain. Values in this graph are whole pixel conversion values evaluatedfrom an average value N_(ave) of the sample data of the average value ofthe pixel value of a plurality of normal brains and a standard deviationNL_(SD) thereof, as described above. That is, these values are obtainedby evaluating a whole pixel conversion value NL_(n)′ of an average valueNL_(n) of the pixel value of the acquired image (sample data) of anormal brain and a whole pixel conversion value AD_(m)′ of an averagevalue AD_(m) of the pixel value of the acquired image (sample data) ofan Alzheimer's diseased brain, respectively, according to the followingequation.

$\begin{matrix}{{{NL}_{n}^{\prime} = {{\frac{1}{{NL}_{SD}} \times {NL}_{n}} - \frac{{NL}_{ave}}{{NL}_{SD}}}},{{AD}_{m}^{\prime} = {{\frac{1}{{NL}_{SD}} \times {AD}_{m}} - \frac{{NL}_{ave}}{{NL}_{SD}}}}} & \left\lbrack {{Equation}\mspace{14mu} 2} \right\rbrack\end{matrix}$

In the example of the graph shown in FIG. 12( a), it is possible todiagnose whether the brain suffers from Alzheimer's disease by setting athreshold value, for example. When a value is larger than the thresholdvalue, the brain is determined to be a normal brain, and when the valueis equal to or less than the threshold value, the brain is determined tosuffer from Alzheimer's disease. In this case, however, even when theexamined person suffers from brain disease, the brain may be determinedto be a normal brain (no brain disease) because the value is larger thanthe threshold value. A ratio by which it is possible to correctlydiagnose that the examined person suffers from brain disease in the caseof presence of brain disease is called sensitivity. Further, even in thecase of a normal brain (no brain disease), there is a case where thepresence of brain disease is determined when the value falls below thethreshold value. A ratio by which it is possible to correctly diagnosethat the examined person is not suffering from brain disease in the caseof a normal brain is called specificity. When the threshold value isdecreased, the specificity is improved (on the other hand, thesensitivity is decreased), and when the threshold value is increased,the sensitivity is improved (on the other hand, the specificity isdecreased).

A graph (called Receiver Operating Characteristic (ROC) curve)indicating a relationship between the sensitivity and the specificity,and the above-described threshold value when the data in FIG. 12( a) isused is shown in FIG. 12( b). In the graph of FIG. 12( b), a horizontalaxis indicates 1-specificity, and a vertical axis indicates thesensitivity. The graph is obtained by plotting values of the sensitivitycorresponding to the threshold value and the 1-specificity. Generally,as the diagnostic outcome, it is preferable that the sensitivity be asclose as possible to 1 and the 1-specificity be as close as possible to0, and thus, a graph in which the plotted line is located in the upperleft as much as possible can demonstrate an excellent diagnosiscapability. For example, when the threshold value is placed atindividual pixel conversion value=−1, the sensitivity is 0.83 and thespecificity is 0.78 (indicated by a circle in the graph), as shown inFIG. 12( b).

The above-described sensitivity and the specificity of each region ofthe brain can also be derived. Graphs indicating a relationship betweenthe threshold value, and the sensitivity and the specificity, for eachregion of the brain shown in FIG. 8, and the sensitivity and thespecificity when the threshold value is placed at the individual pixelconversion value=−1 are shown in FIG. 13. The sensitivity of each regionat this time is as follows:

TABLE 1 ROI (region) Sensitivity Whole brain 0.83 (1) Frontal lobe 0.38(2) Parietal lobe 1.00 (3) Temporal lobe 0.96 (4) Occipital lobe 0.54(5) Cerebellum 0.04 (6) Frontal association area 0.08 (7) Posteriorcingulate gyrus 0.88 (8) Parietal association area 0.96

The above-described sensitivity can be used as the weight coefficient knused when the whole index value is derived. FIG. 14( a) shows a graph inwhich the sensitivity is used as the weight coefficient and the wholeindex value is derived from the sample data of a normal brain and anAlzheimer's diseased brain. Further, a graph showing a relationshipbetween the threshold value, and the sensitivity and the specificity,when the diagnosis is performed using the whole index value, is shown inFIG. 14( b). Similar to the above case, when the threshold value isplaced at the whole pixel conversion value=−1, the sensitivity is 1.00and the specificity is 0.87 (indicated by a circle in the graph), asshown in FIG. 14( b). As described above, it can be seen that thesensitivity of the diagnosis is improved when the diagnosis is performedwhen a plurality of regions are set and the whole index value on whichSUV of each region is weighted is used rather than being determinedusing SUV of the whole brain.

Upon diagnosis using the above-described weight coefficient, thediagnosis unit 15 may employ, as the threshold value, the individualpixel conversion value=−1 used when the weight coefficient is derived,or may also employ the threshold value corresponding to the pointlocated at the upper leftmost along the ROC curve of the whole indexvalue.

Further, in the above-described case, the sensitivity when the thresholdvalue is placed at the individual pixel conversion value=−1 is utilizedas the weight coefficient; however, the sensitivity at a previously setarbitrary value (for example, the individual pixel conversion value=−2),or the sensitivity corresponding to the point located at the upperleftmost along the ROC curve may be utilized, for example. Similarly,the specificity can be utilized as the weight coefficient. Moreover,based on the knowledge of the doctor, etc., the weight coefficientitself may be set previously and arbitrarily.

Moreover, the above may be combined, as described below, to derive theweight coefficient, and the resultant weight coefficient may be used.Weight coefficient kn=kn _(sen) +kn _(spc) +kn _(Dr)In the equation, kn_(sen) denotes the sensitivity calculated using apredetermined threshold value, etc., as described above. kn_(spc)denotes the specificity calculated using a predetermined thresholdvalue, etc., as described above. kn_(Dr) denotes the weight coefficientset by the doctor, etc. Further, any two of the above three values mayalso be used.

Moreover, as the threshold value used for the diagnosis by the diagnosisunit 15, the previously set threshold value (for example, in the abovecase, the whole pixel conversion value=−1) may be used, or the thresholdvalue corresponding to the point located at the upper leftmost along theROC curve, as shown in FIG. 14, obtained by the whole index value whenthe sample data is used may also be used.

The weight coefficient and the threshold value are set before thediagnosis on the examined person by using a plurality of sample dataitems with the brain disease and a plurality of sample data itemswithout the brain disease, as described above. The larger the number ofsample data, the more preferable. Specifically, desirably, about severaltens of examples of the sample data of a normal brain and the brain witha cerebral disease are prepared. Moreover, the sample data of the samein generation and gender as the examined person are desirably used.Further, the sample data is desirably formed of an image acquired at thesame facility or in the same type of machine. Types of brain disease maybe prepared in plural, for example, several tens of examples of thesample data of a brain disease A and several tens of examples of thesample data of a brain disease B. Thus, the configuration of the braindisease diagnosis system 1 according to the embodiment has beenexplained.

Subsequently, by using a flowchart in FIG. 15, processing executed bythe brain disease diagnosis system 1 will be explained. This processingis executed when a brain disease of the examined person is diagnosed bythe brain disease diagnosis system 1.

In the brain disease diagnosis system 1, first, the examined person'sslice image (FDG-PET image) is imaged and acquired by the PET machine123. Further, together with the acquisition of the slice image, theinformation indicating the age of the examined person is input in acorresponding manner (S01). The examined person's slice image taken isinput from the PET machine 123 to the diagnosis server 10 via thegateway server 60 (S02).

In the diagnosis server 10, the slice image is received by the acquiringunit 11. In this case, when the foregoing slice image is an entire bodyimage, the diagnosis server 10 automatically cuts out only a head regionnecessary for the diagnosis (S03). When the foregoing slice image is thehead region only, the automatic extraction of the head region (S03) isnot necessary. Subsequently, the anatomic standardization by the 3DSSPprocessing is performed on the slice image by the acquiring unit 11 sothat the standard brain image is produced (S04). Moreover, the maskprocessing may be performed on the standard brain image by the acquiringunit 11 so as to extract the image data inside a brain.

On the other hand, the imaging processing is further executed by theacquiring unit 11. First, the correction processing is performed on thestandard brain image (S05). The correction processing is performed sothat the average of the pixel values of a whole brain is a previouslyset predetermined value, as described above. Subsequently, the 3DSSPprocessing is performed on the corrected standard brain image by theacquiring unit 11 so as to produce the brain surface projection image(S06). The above-described standard brain image and the brain surfaceprojection image are converted to the acquired image which is input fromthe acquiring unit 11 to the region setting unit 12.

Subsequently, a plurality of regions are set on the standard brain imageand the brain surface projection image by the region setting unit 12(S07). The regions are set based on the previous setting, etc., asdescribed above. Each image data and the set region information areinput from the region setting unit 12 to the individual index valuecalculating unit 13.

Subsequently, the histogram data for each set region, as shown in FIG.10, FIG. 11, etc., is extracted from the image data of theabove-described acquired image by the individual index value calculatingunit 13 (S08). The histogram data is data about the frequency of thepixel for each SUV. Subsequently, the individual index value for eachregion is calculated from the histogram data by the individual indexvalue calculating unit 13 (S09). The calculated individual index valueis input to the whole index value calculating unit 14 from theindividual index value calculating unit 13.

Subsequently, the respective individual index values of the regions areweighted by the whole index value calculating unit 14, and thereby, thewhole index value is calculated (S10). The weight coefficient used forcalculation of the whole index value is previously calculated from theindex value of the sample data having the brain disease and the sampledata not having the brain disease, as described above, and stored in thewhole index value calculating unit 14. However, there is no need thatthe weight coefficient is previously calculated and the coefficient maybe calculated at this timing of S10. Two whole index values arecalculated from the standard brain image and the brain surfaceprojection image. The calculated whole index values are input to thediagnosis unit 15 from the whole index value calculating unit 14.

Subsequently, a brain disease of the examined person is diagnosed basedon the whole index value by the diagnosis unit 15 (S11). Specifically,as described above, by comparing the threshold value previously storedin the diagnosis unit 15 and the whole index value, whether the examinedperson suffers from a brain disease that is subject to examination isdiagnosed. In this case, two diagnoses, i.e., diagnosis based on thestandard brain image and that based on the brain surface projectionimage, are performed. The information indicating the diagnosis outcomeis input from the diagnosis unit 15 to the output unit 16. In this case,if there are a plurality of housed brain disease teacher data items foreach type of brain disease, then the steps in S07 to S11 are repeatedlyexecuted.

Subsequently, the information indicating the diagnosis outcome by thediagnosis unit 15 is output from the output unit 16 to the storages 20and accommodated in the storages 20 (S12). The above-describedinformation accommodated in the storages 20 is acquired by the client100, the viewer 110, etc., of the brain disease diagnosis system 1, anddisplayed. Further, each image or information generated by theabove-described processing is also accommodated in the storages 20, andutilized similar to the information indicating the diagnosis outcome.

On the other hand, by the acquiring unit 11, the imaging processing asdescribed below is further executed on the standard brain imagegenerated in S04. First, from the standard brain image, the brainsurface projection image 204 is generated by the 3DSSP processing (S21).Then, the brain surface projection image of a normal brain for which thegeneration is the same as that of the examined person accommodated inthe storages 20 is acquired (S22). Subsequently, the brain surfaceprojection image of a normal brain is used to calculate the Z score foreach pixel of the brain surface projection image of the examined person.It is noted that the average value and the standard deviation of thepixel values, used upon calculation of the Z score may be calculated atthis timing, or those which are previously calculated and stored in thestorages 20, etc., may be utilized. Subsequently, the brain image(difference image) in which the pixel value is the calculated Z score(the value corresponding thereto) is generated (S23).

Each of the above-described images generated by the acquiring unit 11 isoutput to the storages 20 via the output unit 16 and accommodated in thestorages 20 (S24). Specifically, the slice image (head PET raw image)imaged by the PET machine 123, the anatomically-standardized standardbrain image, the brain surface projection image, and the differenceimage using the Z score are accommodated in the storages 20. Theabove-described information accommodated in the storages 20 is acquiredby the client 100, etc., of the brain disease diagnosis system 1 anddisplayed.

The information and the image accommodated in the storages 20 in stepsS12 and S24 are displayed on the client 100, the viewer 110, etc., asdescribed above. The doctor, etc., refer to (interpret the radiogram of)the information and the image in order to make the most use of these indiagnosis, medical treatment, etc (S31). Thus, the processing executedby the brain disease diagnosis system 1 has been explained.

As described above, in the brain disease diagnosis system 1 according tothe embodiment, based on the image of the brain of the examined person,a brain disease of the examined person is diagnosed. In this system 1, aplurality of regions are set to the brain image, and the individualindex values based on the pixel value are calculated for each of theplurality of regions. Then, each of the individual index values isweighted so that the whole index value is calculated after which theabove-described diagnosis is performed from the whole index value.Therefore, according to the brain disease diagnosis system 1 accordingto the embodiment, it is possible to make a determination whileconsidering the influence of a brain disease for each brain region, andalso possible to diagnose with a manner similar to the doctor'sinterpretation of a radiogram (doctor's eyes). As a result, it ispossible to perform a more accurate and detailed diagnosis of a braindisease. Further, diagnostic mistakes can be decreased. As a result ofthe utilization of the automatic diagnosis outcome, a burden imposedwhen the doctor interprets the radiogram is alleviated and this outcomecan be referred to as additional information when the doctor interpretsthe radiogram.

Moreover, for example, in the method described in Patent Literature 1,the ROI is set only to the site where there is a significant differencebetween the prepared two groups and the other site is not subject to thedetermination. Thus, a case of a brain disease that does not match theROI pattern of the teacher data cannot be easily detected. On the otherhand, in this embodiment, in the case of a brain disease, a site that isspecifically decreased and other sites can be considered as the targetfor determination. Besides, the ROI is set to a site where the doctorseldom observes, and thereby, the data of such a site comes to beconsidered. As a result, it is possible to perform a more accurate anddetailed diagnosis of a brain disease. That is, as compared to theconventional technology, it becomes possible to perform a morecomprehensive, highly accurate diagnosis.

Further, as in the embodiment, when the sample data having the braindisease and that not having the brain disease are utilized and thethreshold value or the weight coefficient used at the time of thediagnosis is obtained, the determination criteria or the weighting canbe made more suitable and the diagnosis of a brain disease can beperformed more accurately and in more detail. For example, a largeweighting can be applied to a range where a brain disease to bediagnosed is greatly influenced. Moreover, a successive numerical valuesuch as the sensitivity and the specificity by using the sample data isemployed, and thus, a successive (score and index value) evaluation,i.e., the level of similarity to the ROI pattern of the teacher data,can be enabled. However, the threshold value or the weight coefficientmay be set by the doctor, etc., and the sample data may not always beused.

If the brain image is corrected, as described above, then a braindisease can be suitably diagnosed by removing individual variability,etc., of the brain of the examined person. Further, if the brain imageis anatomically standardized, then a brain disease can be suitablydiagnosed by facilitating the processing of the brain image.

Moreover, the threshold value or the weighting is desirably applieddepending on the age of the examined person. Although, generally, astate of a brain changes according to age, as described above, accordingto this configuration, it is possible to appropriately diagnose a braindisease according to age.

As in the embodiment, the diagnosis using the brain surface projectionimage is desirable. According to this configuration, based on the brainsurface image that facilitates the diagnosis for some type of braindisease, the diagnosis can be performed. Moreover, in this embodiment,the diagnosis is performed based on the two images, i.e., the standardbrain image and the brain surface projection image; however, thediagnosis may be performed by using either one of the images.

Further, by providing that the modalities 121 to 123 are included in thebrain disease diagnosis system 1, as in the embodiments, the brain imagecan be reliably acquired, and thus, the present invention can bereliably implemented. However, in the brain disease diagnosis system 1,as long as the brain image is acquired, the diagnosis can be performed.Because of this, a device for imaging is not always necessary.

In this embodiment, the case where the PET machine 123 is primarily usedas the modalities has been explained; however, besides, the CT machine121, the MRI machine 122, a SPECT machine (not shown), etc., may beused. In this case, as the brain image, the PET machine 123 can detect ametabolism and a blood flow; the CT machine 121 a brain atrophy, a brainneoplasm, a brain infarction, and a brain bleeding; the MRI machine 122a brain atrophy, a brain neoplasm, a brain infarction, and a brainbleeding; the SPECT machine a brain blood perfusion scintigraphy,respectively. Therefore, the modalities according to a brain disease tobe diagnosed are preferably selected.

Further, in this embodiment, the example in which Alzheimer's disease isdiagnosed has been explained; however, other brain diseases may bediagnosed. For example, a cerebral vascular disturbance, a dementia, anda brain death, or other degenerative neurological disorders orpsychological disorders or encephalitis may be diagnosed. Moreover,regarding the brain atrophy, the brain neoplasm, the brain infarction,and the brain bleeding, it is possible to diagnose which portionexperiences an abnormality.

Further, the brain diseases as described below can be diagnosed (theparenthetic sites below are brain sites closely related to the braindiseases, and can be referred to for the weighting, etc.). Morespecifically, the examples include: frontotemporal dementia (frontallobe, temporal lobe); corticobasal degeneration (frontal lobe, parietallobe); progressive supranuclear palsy (frontal lobe, interior of higherfrontal lobe); amyotrophic lateral sclerosis (frontal lobe, primarysensorimotor area); epilepsy (temporal lobe, others); Alzheimer-typedementia (temporal lobe, parietal lobe, posterior cingulate gyms,hippocampus); mild cognitive impairment (MCI) (temporal lobe, parietallobe, posterior cingulate gyms, hippocampus); dementia with Lewy bodies(parietal lobe, posterior cingulate gyms, occipital lobe); mitochondrialencephalomyopathy (MELAS) (occipital lobe); multiple sclerosis(occipital lobe); Parkinson's disease (occipital lobe); Huntington'sdisease (basal ganglia); Wilson's disease (basal ganglia); Binswanger'sdisease (white matter); hydrocephalus (white matter); cerebrovasculardisease (diffuse (epidemic) decreased cerebellar blood flow);degenerative metabolic defect (spinocerebellar degeneration) (diffusedecreased cerebellar blood flow); multiple system atrophy(olivopontocerebellar degeneration) (diffuse decreased cerebellar bloodflow); dentatorubropallidoluysian atrophy (DRPLA) (diffuse decreasedcerebellar blood flow); cerebrotendinous xanthomatosis (diffusedecreased cerebellar blood flow); medicinal poisoning (diffuse decreasedcerebellar blood flow); cerebellar encephalitis (diffuse decreasedcerebellar blood flow); radiation exposure (diffuse decreased cerebellarblood flow); infectious disease (neurosyphilis, Creutzfeldt-Jakobdisease, Wernicke's encephalopathy) (abnormal spread in a wide cerebralrange); subacute sclerosing panencephalitis (SSPE) (abnormal spread in awide cerebral range); acute disseminated encephalomyelitis (ADEM)(abnormal spread in a wide cerebral range); medicinal poisoning(abnormal spread in a wide cerebral range, a serotonergic system lowersa corpus striatum); hypoxemia (abnormal spread in a wide cerebralrange); carbon monoxide poisoning (abnormal spread in a wide cerebralrange); Binswanger's disease (abnormal spread in a wide cerebral range);Gerstmann's syndrome (parietal lobe); amyotrophic lateral sclerosis(primary sensorimotor area); inferior homonymous quadrantanopia(parietal lobe); hemispatial neglect (parietal lobe in minorhemisphere); ataxia (cerebellum); gait ataxia, truncal ataxia (uppercerebellar vermis); subcortical arteriosclerotic encephalopathy (sitewith blood vessel infarction); other cognition disorders (endocrine,infection, tumor, external injury, and hydrocephalus) (correspondingsite); other functional disorders (hearing, sight, aphasia, semanticaphasia, etc.) (corresponding site); Parkinson's disease (dopaminemedicine (raclopride, β-CFT) lowers a corpus striatum); stimulant addict(serotonin medicine (McN5652, DASB) lowers a corpus striatum);Alzheimer-type dementia (PIB increases integration); and mild cognitiveimpairment (MCI) (PIB increases integration).

REFERENCE SIGNS LIST

1 . . . brain disease diagnosis system, 10 . . . diagnosis server, 11 .. . acquiring unit, 12 . . . region setting unit, 13 . . . individualindex value calculating unit, 14 . . . whole index value calculatingunit, 15 . . . diagnosis unit, 16 . . . output unit, 20 . . . storage,30 . . . load balancer, 40 . . . image server, 50 . . . business server,60 . . . gateway server, 70 . . . switching hub, 80 . . . load balancer,100 . . . client, 110 . . . viewer, 121 to 123 . . . modalities (121 . .. CT machine, 122 . . . MRI machine, 123 . . . PET machine), 130 . . .switching hub.

The invention claimed is:
 1. A brain disease diagnosis system fordiagnosing a brain disease of an examined person, comprising: acquiringmeans for acquiring a brain image of the examined person so as to obtainan acquired image; region setting means for setting a plurality ofregions in the acquired image acquired by the acquiring means;individual index value calculating means for calculating an individualindex value based on a pixel value of the acquired image, in each of theplurality of regions set by the region setting means; whole index valuecalculating means for calculating a whole index value by weighting theindividual index value of each of the plurality of regions calculated bythe individual index value calculating means; diagnosis means fordiagnosing the brain disease of the examined person based on the wholeindex value calculated by the whole index value calculating means; andoutput means for outputting information indicating a diagnosis outcomeissued by the diagnosis means.
 2. The brain disease diagnosis systemaccording to claim 1, wherein the diagnosis means diagnoses the braindisease of the examined person by comparing a threshold value obtainedbased on an index value of sample data having the brain disease and anindex value of sample data not having the brain disease and the wholeindex value calculated by the whole index value calculating means. 3.The brain disease diagnosis system according to claim 1, wherein thewhole index value calculating means performs the weighting based on theindex value of the sample data having the brain disease and the indexvalue of the sample data not having the brain disease.
 4. The braindisease diagnosis system according to claim 1, wherein the acquiringmeans corrects the image based on the pixel value of the acquired brainimage so as to obtain the acquired image.
 5. The brain disease diagnosissystem according to claim 1, wherein the acquiring means also acquiresinformation indicating the age of the examined person, and the diagnosismeans diagnoses the brain disease of the examined person according tothe age of the examined person indicated by the information acquired bythe acquiring means.
 6. The brain disease diagnosis system according toclaim 1, wherein the acquiring means anatomically standardizes theacquired brain image so as to obtain the acquired image.
 7. The braindisease diagnosis system according to claim 1, further comprisingimaging means for imaging the brain image of the examined person,wherein the acquiring means acquires the image imaged by the imagingmeans.
 8. The brain disease diagnosis system according to claim 7,wherein the imaging means images a slice image of a brain of theexamined person as the brain image of the examined person, and theacquiring means produces from the slice image imaged by the imagingmeans a brain surface projection image obtained by projecting a brainsurface of the brain so as to obtain the acquired image.